Devices, systems and processes for providing adaptive audio environments

ABSTRACT

Devices, systems and processes for providing an adaptive audio environment are disclosed. For an embodiment, a system may include a wearable device and a hub. The hub may include an interface module configured to communicatively couple the wearable device and the hub and a processor, configured to execute non-transient computer executable instructions for a machine learning engine configured to apply a first machine learning process to at least one data packet received from the wearable device and output an action-reaction data set and for a sounds engine configured to apply a sound adapting process to the action-reaction data set and provide audio output data to the wearable device via the interface module. The audio output data may be utilized by the wearable device to provide an adaptive audio environment.

TECHNICAL FIELD

The technology described herein generally relates to devices, systemsand processes for providing adaptive audio environments.

BACKGROUND

Devices, systems and processes are needed for providing adaptive audioenvironments. As used herein, “user(s)” and/or “listener(s)” are usedinterchangeably to refer to those then immersed in an environment. Suchan environment may include various conditions that can be detected,analyzed, monitored and otherwise processed by use of vision, audio,presence, motion, speed, location, mental and other sensors, andexternal data. Such an environment is referred to herein as a user's“total environment.” A total environment may arise at any given time andanywhere a user is present, such as on a sidewalk, in an automobile,home, office, store, mall, train, bus, or otherwise. The totalenvironment may include sub-environments such as an audio environmentand a visual environment. As is commonly known, a visual environment maybe augmented by visual indicators, such an augmented environmentcommonly being referred to as an augmented reality.

Likewise, an audio environment may be augmented by one or more audiosystems, such as those provided in a car, home, public space (e.g., byway of public address systems), in-ear and/or over-ear speakers, and thelike. With the prevalence today of commonly available audio systems,situations often arise where a given user's then arising audioenvironment is not optimized for a then arising total environment. Atone extreme, non-optimization can result in unharmful conditions whichfacilitate user inefficiencies, disengagement, or other conditions. Forexample, a user reading a book may be distracted by daydreams or otherthoughts unrelated to the content of the book. At another extreme,non-optimization may result in harmful and/or dangerous situationsoccurring, such as a user not recognizing an otherwise perceivablethreat condition arising, such as vehicular hazards or otherwise. Yet,today no devices, systems or methods are available for detecting auser's then arising or near future arising total environment andproviding, in response and/or in anticipation thereto, an adaptive audioenvironment. Thus, a need exists for devices, systems and processes forproviding adaptive audio environments. A need also exists for devices,systems and processes for providing adaptive audio environments that areoptimized in view of a then arising or near future arising user's totalenvironment.

SUMMARY

The various embodiments of the present disclosure relate in general todevices, systems and processes for providing adaptive audioenvironments. In accordance with at least one embodiment of the presentdisclosure, a system for providing an adaptive audio environment mayinclude a wearable device and a hub. The hub may include an interfacemodule configured to communicatively couple the wearable device and thehub. The hub may include a processor, configured to executenon-transient computer executable instructions for a machine learningengine configured to apply a first machine learning process to at leastone data packet received from the wearable device and output anaction-reaction data set. The hub may include a processor, configured toexecute non-transient computer executable instructions for a soundsengine configured to apply a sound adapting process to theaction-reaction data set and provide audio output data to the wearabledevice via the interface module. The audio output data may be utilizedby the wearable device to provide an adaptive audio environment.

For at least one embodiment, the wearable device may be configured toexecute non-transient computer instructions for a wearable device inputprocess configured to process first sensor data received from a firstsensor. The first sensor data may include image data. The wearabledevice input process may be further configured to process at least onesecond sensor data received from at least one second sensor. For atleast one embodiment, each of the first sensor data and the at least onesecond sensor data may be provided by at least one of an image sensor, asound sensor, a physio sensor, a location sensor, and a motion sensor.

For at least one embodiment of a system for providing an adaptive audioenvironment, a wearable device input process may include applying a datapacketization process to each of the first sensor data and the at leastone second sensor data. A result of the data packetization process maybe output by the wearable device to the hub as the at least one datapacket.

For at least one embodiment of a system for providing an adaptive audioenvironment, the wearable device may include at least one sensor and atleast one audio output device.

For at least one embodiment of a system for providing an adaptive audioenvironment, the machine learning engine may be configured to usefeedback data when applying a second machine learning process to atleast one second data packet received from the wearable device andoutput a second action-reaction data set. The sounds engine may befurther configured to apply a second sound adapting process to thesecond action-reaction data set and provide second audio output data tothe wearable device via the interface module. The second audio outputdata may be further utilized by the wearable device and the at least oneaudio output device to provide the adaptive audio environment.

For at least one embodiment of a system for providing an adaptive audioenvironment, the wearable device may be configured to executenon-transient computer instructions for a wearable device outputprocess. The wearable device output process may be configured to processthe audio output data for presentation to a user by the at least oneaudio output device.

For at least one embodiment of a system for providing an adaptive audioenvironment, the interface module may be further configured tocommunicatively couple the hub with at least one server. The server maybe configured to communicate external data to the hub. Based on theexternal data, the machine learning engine may be further configured toapply a second machine learning process to the at least one data packetreceived from the wearable device and output a second action-reactiondata set.

In accordance with at least one embodiment of the present disclosure, asoftware architecture, encoded on at least one non-transitory computerreadable medium for providing an adaptive audio environment may includea wearable device input process. The wearable device input process mayinclude non-transient computer instructions, executable by a wearabledevice, for processing first sensor data received from a first sensorand outputting at least one data packet. A wearable device outputprocess may be used and include non-transient computer instructions,executable by the wearable device, for receiving and processing audiooutput data into at least one sound for use in providing an adaptiveaudio environment to a user of the wearable device. The audio outputdata may be received from and result from a processing of the at leastone data packet by the hub.

For at least one embodiment of the software architecture, the firstsensor data may be received from at least one of an image sensor, asound sensor, a physio sensor, a location sensor, and a motion sensor.

For at least one embodiment of the software architecture, a machinelearning process may include non-transient computer instructions,executable by the hub, for processing the at least one data packet intoan action-reaction data set.

For at least one embodiment of the software architecture, a soundadapting process may include non-transient computer instructions,executable by the hub, for processing the action-reaction data set intoaudio output data.

For at least one embodiment of the software architecture, the machinelearning processes may utilize user preference data stored in a userpreference database to process the at least one data packet int anaction-reaction data set.

For at least one embodiment of the software architecture, the soundadapting process may utilize sound data stored in a sounds database toprocess the action-reaction data set into the audio output data.

For at least one embodiment of the software architecture, at least oneof the machine learning process and the sound adapting process mayinclude at least one supervised machine learning algorithm trained usingan initial default data set corresponding to a generic totalenvironment.

In accordance with at least one embodiment of the present disclosure, amethod for providing an adaptive audio environment may includeoperations performed in a wearable device and in a hub device. For atleast one embodiment, operations performed in a wearable device mayinclude receiving sensor data, processing the sensor data using at leastone wearable device input process into a data packet, outputting thedata packet to a hub device, receiving audio output data from the hubdevice and applying at least one wearable device output process to thereceived audio output data to produce at least sound providing anadaptive audio environment for a user. For at least one embodiment,operations performed in a hub device may include receiving the datapacket from the wearable device, applying at least one hub process tothe data packet and outputting the audio output data. The audio outputdata may result from the applying of the at least one hub process to thedata packet.

In accordance with at least one embodiment of the present disclosure, amethod for providing an adaptive audio environment may include use ofsensor data that includes first sensor data received from an imagesensor and second sensor data received from at least one of a soundsensor, a physio sensor, a location sensor and a motion sensor.

In accordance with at least one embodiment of the present disclosure, amethod for providing an adaptive audio environment may includeoutputting at least one sound that is responsive to an image provided inthe first sensor data.

In accordance with at least one embodiment of the present disclosure, amethod for providing an adaptive audio environment may include use ofsecond sensor data that includes physio data for the user. For at leastone embodiment, the at least one sound may be responsive to the physiodata.

BRIEF DESCRIPTION OF THE DRAWINGS

The features, aspects, advantages, functions, modules, and components ofthe devices, systems and processes provided by the various embodimentsof the present disclosure are further disclosed herein regarding atleast one of the following descriptions and accompanying drawingfigures. In the appended figures, similar components or elements of thesame type may have the same reference number and may include anadditional alphabetic designator, such as 108 a-108 n, and the like,wherein the alphabetic designator indicates that the components bearingthe same reference number, e.g., 108, share common properties and/orcharacteristics. Further, various views of a component may bedistinguished by a first reference label followed by a dash and a secondreference label, wherein the second reference label is used for purposesof this description to designate a view of the component. When only thefirst reference label is used in the specification, the description isapplicable to any of the similar components and/or views having the samefirst reference number irrespective of any additional alphabeticdesignators or second reference labels, if any.

FIG. 1 is schematic diagram of a system for providing an adaptive audioenvironment in accordance with at least one embodiment of the presentdisclosure.

FIG. 2 is a schematic diagram of a software architecture configured foruse in providing an adaptive audio environment in accordance with atleast one embodiment of the present disclosure.

FIG. 3 is a flow chart illustrating a process for providing an adaptiveaudio environment in accordance with at least one embodiment of thepresent disclosure.

DETAILED DESCRIPTION

The various embodiments described herein are directed to devices,systems and processes for providing adaptive audio environments. Asdescribed herein, the various embodiments of the present disclosure aredirected to detecting, analyzing and processing one or more aspects of auser's then arising total environment and, in response thereto,providing an adaptive audio environment.

Various embodiments of the present disclosure may also be directed toproviding an adaptive audio environment that is future looking and seeksto optimize the user's then arising current audio environment in view ofan anticipated, planned, predictable, or otherwise reasonablydeterminable future total environment. As used herein a reasonablydeterminable future total environment is one arising where theprobability the future total environment arising is greater than 90percent. A non-limiting example of such a future total environment, inview of which the various embodiments of the present disclosure mayoptimize a user's current audio environment, is a sports star preparingfor an athletic event; where the adaptive audio environment providedseeks to maximize the sports star's concentration on the upcoming event,while minimizing other distractions. Another non-limiting example ofsuch a future total environment, in view of which the variousembodiments of the present disclosure may provide an adaptive usercurrent audio environment, include a user walking along a non-busysidewalk approaching a busy intersection, the user's then current audioenvironment may provide an adapted audio profile requiring less userattention to their surroundings, which audio profile adjusts as the userapproaches the busy intersection to facilitating more user attention totheir surroundings.

In accordance with at least one embodiment, a user's audio environmentmay be adapted by use of any audible device(s). The audible device(s)may be personal to the user, and non-limiting examples of such audibledevices include in-ear and over-ear speakers, such as headphones, earbuds and the like. Likewise, audible devices may be non-personal withrespect to a given user and may be directed to an enclosure, such asspeakers in a car or home, or to an environment, such as speakers in apublic facility, train, bus, office, or otherwise. For at least oneembodiment, adaptation of an audio environment may include adjustmentsto either and/or both of personal audible devices and/or non-personalaudible devices.

As shown in FIG. 1, for at least one embodiment of the presentdisclosure, a system 100 for providing an adaptive audio environment mayinclude a hub 102 and a wearable device 118.

Hub

The hub 102 may include one or more processor modules 104 configured toprovide, at least, a machine learning engine 106 and a sounds engine108. A processor module 104 may be configured from any desired dataand/or signal processing capabilities. For at least one embodiment, aprocessor module 104 may have access to one or more non-transitoryprocessor readable instructions, including instructions for executingone or more applications, engines, and/or processes configured toinstruct the processor to perform computer executable operations(hereafter, “computer instructions”). A processor module 104 may use anyknown or later arising processor capable of providing and/or supportingthe features and functions of the hub 102 as needed for any givenintended use thereof and in accordance with one or more of the variousembodiments of the present disclosure. For at least one non-limitingembodiment, a processor module 104 is configured as and/or has thecapabilities of a 32-bit or 64-bit, multi-core ARM based processor. Forat least one embodiment, the hub 102 may arise on one or more backendsystems, such as server systems or otherwise.

A processor module 104 may be configured to execute computerinstructions and/or data sets obtained from one or more storage modules112. As further shown in FIG. 1 and for at least one embodiment of thepresent disclosure, a hub 102 may include an interface module 110. Theinterface module 110 may include one or more remote interface modules110(B). For at least one embodiment, the storage modules 112 may includeuse of remote storage devices 134 that may be communicatively coupled tothe hub 102 by use of the one or more remote interface modules 110(B)and a network connection 130 and/or via one or more direct connection(s)132, or combinations thereof. It is to be appreciated that the storagemodules 112 and/or remote storage devices 134 (each a “storagecomponent”) may be configured using any known or later arising datastorage technologies. In at least one embodiment, a storage componentmay be configured using flash memory technologies, micro-SD cardtechnology, as a solid-state drive, as a hard drive, as an array ofstorage devices, or otherwise. A storage component may be configured tohave any desired data storage size, read/write speed, redundancy, orotherwise. A storage component may be configured to providetemporary/transient and/or permanent/non-transient storage of one ormore data sets, computer instructions, and/or other information. Datasets may include, for example, information specific to a user, such asthose provided by a user preference database 114, information relatingto one or more sounds, such as those provided by a sounds database 116,or other information. Computer instructions may include firmware andsoftware instructions, and data for use in operating the hub 102. Suchdata sets may include software instructions configured for execution bythe processor module 104, another module of the hub 102, a wearabledevice 118, or otherwise. Such computer instructions provide computerexecutable operations that facilitate one or more features or functionsof a hub 102, a wearable device 118, or otherwise. A storage componentmay be further configured to operate in conjunction with one or moreservers 136. The one or more servers 136 may be coupled to the hub 102,a wearable device 118, remote storage devices 134, other devices thatare internal and/or external device to the hub 102 and/or the wearabledevice 118, or otherwise. The server(s) 136 may be configured to executecomputer instructions which facilitate in whole, or in part, theproviding of an adaptive audio environment in accordance with at leastone embodiment of the present disclosure. For at least one embodiment,one or more of the storage components may be configured to store onemore data sets, computer instructions, and/or other information inencrypted form using known or later arising data encryptiontechnologies.

As further shown in FIG. 1 and for at least one embodiment of thepresent disclosure, the interface module 110 may include a wearableinterface module 110(A). The wearable interface module 110(A) may beconfigured to facilitate communications between the hub 102 and awearable device 118. The interface module 110 may utilize any known orlater arising technology to establish, maintain, and operate one or morelinks between the hub 102, the wearable device 118 and remote servers136, remote storage devices 134, or other devices. Such links may arisedirectly, as illustratively shown by direct connection 132, or usingnetwork or other communications technologies, as illustratively shown bynetwork 130. Non-limiting examples of technologies that may be utilizedto facilitate such communications in accordance with one or moreembodiments of the present disclosure include, but are not limited to,Bluetooth, ZigBee, Near Field Communications, Narrowband IOT, WIFI, 3G,4G, 5G, cellular, and other currently arising and/or future arisingcommunications technologies. The interface module 110 may be configuredto include one or more data ports for establishing connections between ahub 102 and another device, such as a laptop computer. Such data portsmay support any known or later arising technologies, such as USB 2.0,USB 3.0, ETHERNET, FIREWIRE, HDMI, and others. The interface module 110may be configured to support the transfer of data formatted using anydesired protocol and at any desired data rates/speeds. The interfacemodule 110 may be connected to one or more antennas (not shown) tofacilitate wireless data transfers. Such antenna may support short-rangetechnologies, such as 802.11a/c/g/n and others, and/or long-rangetechnologies, such as 4G, 5G, and others. The interface module 110 maybe configured to communicate signals using terrestrial systems,space-based systems, and combinations thereof systems. For example, ahub 102 may be configured to receive GPS signals from a satellitedirectly, by use of a wearable device 118, or otherwise.

The processor module 104 may be configured to facilitate one or morecomputer engines, such as a machine learning engine 106 and a soundengines 108. As discussed in further detail below, each of these enginesare configured to execute at least one computer instruction. For atleast one embodiment, the machine learning engine 106 provides thecapabilities for the hub 102 and wearable device 118 to determine andoutput one or more data sets identifying at least one characteristic ofa desired adaptive audio environment, at a given time, in view of atleast one of a then or future arising total environment, one or moreuser preferences, one or more wearable device capabilities, and thelike. For at least one embodiment, the sounds engine 108 provides thecapabilities for the hub 102 to identify and provide an adaptive audioenvironment, at any given time, in view of the one or more data setsoutput by the machine learning engine. As used herein, it is to beappreciated that music and sounds are used interchangeably, with theformer being a collection of the latter. Further, it is to beappreciated that sounds provided by one or more of the variousembodiments of the present disclosure are generally those provided afrequencies within 20 Hz and 20 kHz—which is the typical human soundperception range. However, other frequencies may be utilized for one ormore embodiments of the present disclosure. The machine learning engine106 may utilize data obtained from a storage component, such as dataprovided in a user preference database 114 and/or in a remote storagedevice 134. The sounds engine 108 may utilize data obtained from astorage component, such as data sets provided in a sounds database 116and/or in a remote storage device 134. As illustratively shown in FIG.1, a user preference database 114 and a sounds database 116 may beprovided local to a hub 102 in the storage module 112. It is to beappreciated, however, that a user preference database 114 and/or soundsdatabase 116 may be provided and/or augmented, in whole or in part,using data stored in a wearable device 118. Further, it is to beappreciated that data sets in any given storage component may beupdated, augmented, replaced or otherwise managed, in whole or in part,based upon any then arising or future arising audio environment to begenerated for a given user.

Wearable Device

As further shown in FIG. 1 and for at least one embodiment of thepresent disclosure, a wearable device 118 is configured to capture,monitor, detect and/or process a user's then arising current environmentand, in response thereto and/or in anticipation thereof, provide anadaptive audio environment. For at least one embodiment, a wearabledevice 118 may be provided by a smartphone, smartwatch, tablet, laptopcomputer, personal computer, fitness tracker, or similar personalcomputing device. For at least one embodiment, the wearable device 118may include various sensors. Sensors, individually and/or collectively,may be utilized to identify, characterize, define, monitor and otherwiseassess one or more aspects of a user's then arising total environment.Non-limiting examples of such sensors include image sensors 120, soundssensors 122, physio sensors 124, location sensors 126, and motionsensors 128. Other sensors (not shown) may be utilized. Any desiredtypes, combinations, permutations and/or configurations of one or moresensors may be used in a desired implementation of an embodiment of thepresent disclosure.

For at least one embodiment, a wearable device 118 may include one ormore image sensors 120 configured to capture, monitor, detect and/orprocess image-based aspects of a user's total environment. Image sensors120 may operate with respect to motion, still, intermittent, or otherimage capture protocols. Image sensors 120 may be provided for anydesired field-of-view, such as a frontal view, a 360-degree view, orotherwise. Image sensors 120 may provide for visual field monitoring inany desired wavelengths of the electromagnetic spectrum, including butnot limited to visible wavelengths, infra-red wavelengths, andotherwise. Wavelengths and/or spectrums utilized by an image sensor 120may vary based upon a then arising total environment, time of day, userpreference, or otherwise. For example, during daytime, an image sensor120 may utilize human visible wavelengths, whereas during nighttime,foggy or other diminished light conditions, infrared or otherwavelengths may be additionally and/or alternatively utilized. Imagescaptured by an image sensor 120 may be proceed by image processingcapabilities provided by an image sensor 120 itself, or by capabilitiesprovided, in whole or in part or in combination with any of the wearabledevice 118, the hub 102, a server 136, or otherwise.

For at least one embodiment, a wearable device 118 may include one ormore sound sensors 122 configured to capture, monitor, detect and/orprocess sound-based aspects of a user's current total environment.Sound-based aspects may include then external sounds (e.g., honkinghorns, sound patterns (e.g., human speech), or otherwise, as well asinternal sounds, such as those generated by a wearable device 118.External and internal sounds may arise persistently, intermittently orotherwise. Sound sensors 122 may be provided for any desiredsound-field, such as a frontal field, a 360-degree field, or otherwise.Sound sensors 122 may provide for sound field monitoring, filtering,processing, analyzing and other operations at any one or morefrequencies or ranges thereof. Sound sensors 112 may be configured tofilter, enhance, or otherwise process sounds to minimize and/oreliminate noise, enhance certain sounds while minimizing others, or asotherwise desired as then configured by a user, a computer instructionexecuting by a wearable device 118 or a hub 102, or otherwise. Soundscaptured by a sound sensor 122 may be proceed by sound processingcapabilities provided by the sound sensor 122, the wearable device 118,the hub 102, a server 136, and/or any desired combinations orpermutations of the foregoing.

For at least one embodiment, a wearable device 118 may include one ormore physio sensors 124 configured to capture, monitor, detect and/orprocess physiological-based aspects of a user's total environment(herein, “physio-aspects”). Examples of such physio-aspects include auser's perspiration, heart rate, blood pressure, brain waves, forcesexerted by or on the user, and otherwise. Physio-aspects may arisepersistently, intermittently or otherwise. Data from physio sensors 124may be processed by processing capabilities provided by the physiosensor 124, the wearable device 118, the hub 102, a server 136, and/orany desired combinations or permutations of the foregoing. One exampleof a physio sensor that may be configured for use with at least oneembodiment of the present disclosure is an EMOTIV EPOC+14 CHANNEL MOBILEEEG™, manufactured by Emotiv Inc. For at least one embodiment, a physiosensor 124 may be configured to monitor, capture, detect and/or processa user's emotions, as represented by brain waves, or otherphysio-psychological indicators, such as perspiration, sudden movementsof one or more body parts, or otherwise.

For at least one embodiment, a wearable device 118 may include one ormore location sensors 126 configured to capture, monitor, detect and/orprocess a user's location in its total environment. Examples of locationsensors include those using global positioning satellite (GPS) systemdata and otherwise. Data from location sensors 126 may be processed byprocessing capabilities provided by the location sensor 126, thewearable device 118, the hub 102, a server 136, and/or any desiredcombinations or permutations of the foregoing.

For at least one embodiment, a wearable device 118 may include one ormore motion sensors 127 configured to capture, monitor, detect and/orprocess a user's change of motion or orientation, such as byacceleration, deceleration, rotation, inversion or otherwise in a givenuser's current total environment. A non-limiting example of a motionsensor is an accelerometer. Data from motion sensors 127 may beprocessed by processing capabilities provided by the motion sensor 127,the wearable device 118, the hub 102, a server 136, and/or any desiredcombinations or permutations of the foregoing.

For at least one embodiment, a wearable device 118 may include one ormore audio output device(s) 128 configured to provide audible sounds toa user. Examples of audio output device(s) 128 include one or more earbuds, headphones, speakers, cochlear implant devices, and the like.Audible signals to be output by a given audio output device 128 may beprocessed using processing capabilities provided by the audio outputdevice 128, the wearable device 118, the hub 102, a server 136, and/orany desired combinations or permutations of the foregoing. For at leastone embodiment, audio output devices 128 are personal to a given user.In other embodiments, audio output devices 128 may be configured toinclude non-personal audio devices which provide audio signals to otherpersons or objects, such as one or more persons within a desiredproximity of the user of the wearable device 118, animals or otherwise.Examples of such non-personal audio output devices include speakers,horns, sirens, and the like. Sounds output by non-personal audio devicesmay or may not be perceptible by a user of the wearable device 118.

For at least one embodiment, a wearable device 118 may include one ormore user module(s) 129 configured to provide an interface between thewearable device 118 and the user. The user module 129 may be configuredto include user input and output interfaces, such as by use of buttons,touch screens, voice, motion, visual displays, haptic feedback, orotherwise for use in facilitating user control and/or operation of awearable device 118.

In FIG. 2, one embodiment of a software architecture 200 for use withone or more embodiments of the present disclosure is shown. As shown, awearable device 118 and hub 102 include one or more processes eachincluding computer instructions, for processing data output by one ormore sensors and, in response thereto, provide an adaptive audibleenvironment. The processes of FIG. 2 are depicted with respect to theuse of data from one or more sensors providing, for example, image data204, sound data 206, physio data 208, location data 210, and motion data212. It is to be appreciated, that the various embodiments of thepresent disclosure may be configured for use in conjunction with dataprovided by as few as one sensor and/or in conjunction with data fromany number of two or more or sensors and/or external data from anexternal source, such as data from a hub 102, or a server 136, and inany desired combinations or permutations thereof. The sensor data may beprocessed, in whole or in part, by a wearable device 118 by executingone or more wearable device input processes 202, such as a datapacketization process 214 whereby a wearable device 118 collects a setof data provided by one or more sensors at a given time, and/or over adesired period, and packetizes such data for processing by the hub 102.

For at least one embodiment, wearable device input processes 202 forimage data 204 may include collecting an image on a pre-determinedinterval, such as every “I” milliseconds, where “I” varies based on thetype of (in)activity in which a user of a wearable device 118 is thenengaging and/or in view of current total environment characteristics,where such current total environment characteristics may be userspecified, based on machine learning processes 218, determined by awearable device based on sensor data, or otherwise determined. Thisprocess may result in the generation of image data 204 that can befurther processed by the data packetization process 214 to eliminateredundant and/or unnecessary image data. For example, a wearable device118 used by user then engaging in the driving of a vehicle may beconfigured to automatically use a different image capture rate, andimage processing routines differently than are used when the user isengaged in a passive activity, such as the reading of a book.Accordingly, it is to be appreciated that image data 204 provided by animage sensor 120 and processed by a wearable device 118 may vary basedon one or more factors, including activity type, image data type (e.g.,visible light vs infra-red), then arising total environment, anticipatedfuture total environment, or otherwise.

Further, the wearable device processes 202 may include adjustingoperating characteristics of an image sensor 204 and image data 204generated using local image data processing. For example, a wearabledevice 118 may be configured to recognize that a user is engaged inreading activities, such recognition being based, for example, upon afrequency of change in an image captured by an image sensor 120, or uponan instruction provided by a user input provided via user module 129.When so configured, an image sensor 120 may be configured to engage ineyeball tracking operations, such that words read over a given timeperiod can be collected and packetized for providing to the hub 102,with further analysis being accomplished by one or more hub processes216 such as machine learning processes 218 and/or by a server 136.

Similar processes may arise with other sensor data, including sound data206. More specifically and for at least one embodiment, wearable deviceinput processes 202 may include computer instructions directed to thecapturing of sound data 206 as desired, such as at a given instance oftime, on a desired interval, or otherwise. A desired interval may be,for example, every “S” milliseconds, where “S” varies based on the typeof (in)activity in which a user of a wearable device is then engaging,user settings, user preferences, external data, and/or one or morecharacteristics of a current total environment or a future totalenvironment, where such current or future total environmentcharacteristics may be user specified, based on machine learningprocesses 218, determined by a wearable device based on sensor data,based on external data, or otherwise determined. For example, in astatic total environment where a steady background noise exists, such ason an airplane, sound data 206 may be provided at a lower frequency tothe data packetization processes 214 as compared to when a user is in adynamic total environment, such as on a trading pit of a commoditiesexchange.

Similar processes may arise with other sensor data, including physiodata 208. More specifically and for at least one embodiment, wearabledevice input processes 202 may include those directed to capturingphysio data 208 at a given time and/or on a given interval. The giveninterval may be, for example, every “P” milliseconds, where “P” variesbased on the type of (in)activity in which a user of a wearable deviceis then engaging and/or other current or future total environmentcharacteristics. Such current or future total environmentcharacteristics may be user specified, based on machine learningprocesses 218, determined by a wearable device based on sensor data,based on external data, or otherwise determined. For example, physiodata 208 processed by a wearable device 118 may range across a widegamut of physio data types and include basic physiological indicators,such as heart rate, temperature, respiratory rate, and the like. Physiodata types may also include intermediate physiological indicators, suchas indications of a user's mental alertness with such alertnesspotentially varying based upon a user's individual characteristicsand/or then arising of future arising characteristics of a user's totalenvironment, such as currently being tired or rested or in the futureneeding to rest. Physio data types may also include advancedphysiological indicators, such as the emotional state of a user at agiven time, non-limiting examples of such emotional states may includebeing happy, sad, indifferent, placid, frightened, at rage, orotherwise. The wearable device input processes 202 may be configured tocapture such physio data 208 and using data packetization processes 214process the data into a desired set of data responsive to the thenarising current total environment or a desired future arising currentenvironment for the user. For example, the wearable device processes 202may be configured to generate a first data set for a user in a flightcondition, driving a motor vehicle that is different than a second dataset for a user calmly reading the comics. Similarly, wearable deviceprocesses 202 may be configured to prepare a user mentally for a futurearising current environment, such as a rest period after an athleticendeavor, in view of a user's then detected current mental and/orphysiological state.

Similar processes may arise with other sensor data, including locationdata 210. More specifically and for at least one embodiment, wearabledevice input processes 202 may include those directed to capturinglocation data 210 on a then desired interval. The desired interval maybe, for example, every “L” milliseconds, where “L” varies based on thelocation of the user and type of (in)activity in which a user of awearable device is then engaging and/or in view of other currentenvironment characteristics. For example, location data 210 for a staticuser, e.g., one reading a book in their home, may be processed andpacketized by data packetization process 214 for communication to thehub 102 on a less frequent basis than location data 210 is processed andpacketized for a then traveling user.

Similar processes may arise with other sensor data, including motiondata 212. More specifically and for at least one embodiment, wearabledevice input processes 202 may include those directed to capturingmotion data 212 on a then desired interval. The desired interval may be,for example, every “M” milliseconds, where “M” varies based on the typeof (in)activity in which a user of a wearable device is then engagingand/or other current or desired future total environmentcharacteristics, where such current and/or future total environmentcharacteristics may be user specified, based on machine learningprocesses 218, determined by a wearable device based on sensor data,based on external data, or otherwise determined. For example, motiondata 212 processed by a wearable device input process 202 for a userengaged in an athletic pursuit, such as a bicycle journey or rowingsojourn, may vary from motion data 212 processed for a user engaged inreading a book. It is to be appreciated that motion data may includevarious types of data including acceleration, deacceleration,orientation changes, speed changes, relative speed, rate of ascent ordecent, slope, and the like. Such motion data may arise independent ofand/or in view of location data, for example, motion data for a downhillskier versus motion data versus that for a track runner may vary due tothe topography indicated by location data.

Similar processes may arise with regards to other data sets, includingdata sets not provided by a sensor on or communicatively coupled to awearable device 118. For example, external data may include weather datamay be provided to a wearable device and used by one or more wearabledevice input processes 202 in determining which types and the frequencyat which one or more other forms of sensor data are packetized by datapacketization process 214. For example, weather data providingindications of severe conditions may result in greater monitoring of auser's physio data to verify their current health is not undesirablystressed. Similarly, external data may include calendar and otherindications of planned future events, in anticipation of which thesystem 100 may provide an adaptive audio environment directed toenhancing the user's participation in, enjoyment of, or otherwise ofsuch future event. For at least one embodiment, wearable device inputprocesses 202 may include those directed to processing such externaldata on a then desired interval. The desired interval may be, forexample, every “O” milliseconds, where “O” varies based on the type of(in)activity in which a user of a wearable device is then engagingand/or other current and/or future total environment characteristics,where such current and/or future total environment characteristics maybe user specified, based on machine learning processes 218, determinedby a wearable device based on sensor data, or otherwise determined. Itis to be appreciated that each of the periods I, S, P, L, M and O mayoverlap, occur jointly, independently, vary in length, occur many timesa second, once a minute, or otherwise, may start at a similar time orotherwise occur, for any given user, and in view of any current orfuture arising total environment.

As shown in FIG. 2, the software architecture 200 may include one ormore hub processes 216 that are executed by a hub 102, such hubprocesses 216 include computer instructions. As discussed above, eachhub 102 may be configured to include a machine learning engine 106executing computer instructions facilitating one or more machinelearning processes 218. It is to be appreciated that the one or moremachine learning processes 218 utilized by hub 102 to facilitate theproviding of an adaptive audio environment to a user will vary basedupon the current and/or future total environment, as such totalenvironment being represented in the information (“data packets”)provided by a wearable device's data packetization processes 214. Asshown in FIG. 2, a bi-directional connection may be provided betweenwearable device input processes 202, which include data packetizationprocess 214, and hub processes 216.

For at least one embodiment, a first non-limiting example of a machinelearning process 218 executed by a machine learning engine 106 mayinclude the processing of images to recognize objects, environments,words, colors, people, locations, and otherwise. For example, a userriding a bicycle may result in a machine learning process 218recognizing and analyzing the status of moving objects such as vehiclesand pedestrians, while providing less analysis for fixed objects, suchas buildings. Similarly, machine learning processes 218 for a bicyclistmay be configured to analyze sound data 206 indicative of an impendingor actual danger, such as honking car horn, while disregardingnon-threatening sounds, such as a normal street noise. Machine learningprocesses 218 for such a current total environment may also includeanalyzing sound data 206 to detected words, sounds environments, songs,or otherwise. Similarly, machine learning processes 218 for such acurrent total environment may include computer instructions foranalyzing physio data 208 indicative of such cyclist current condition.Certain data, such as physio data 208 indicating the bicyclist isexceeding a desired heart rate or other physiological parameter, may beprocessed differently than other data. Similarly, machine learningprocesses 218 for such a bicyclist may be configured to analyze physiodata 208 indicative of the cyclist's then arising mental state, such asanger, fear, caution, lack thereof, or otherwise. Similarly, machinelearning processes 218 for such bicyclist may be configured to analyzelocation data 208. Such location data 208 may be indicative of acyclist's compliance with speed limits, traffic rules, type of roadway,impending obstacles, or otherwise. Similarly, machine learning processes218 for such bicyclist may be configured to analyze motion data 212indicative of a cyclist's then speed and orientation (e.g., have theyfallen or are still upright). Any of the available data may beprioritized, ignored or otherwise processed by the machine learningprocesses 218 based upon a current total environment, a future totalenvironment, or elements of such environments.

Based upon one or more of the various data packets provided by awearable device 118 to a hub 102, external data and/or one or moremachine learning processes 218, the hub processes 216 may be configuredto generate an action-reaction data set (an “ARDS”). For at least oneembodiment, an ARDS may be generated by a machine learning process 218based upon external data provided from a remote data source such as aserver 136 and/or remote storage device 134. The external data providedto a machine learning process 218 may be the same as, an update to,and/or different data that is provided, if any, to a wearable device118. It is to be appreciated that a machine learning process may beconfigured to access one or more remote data processes 222 to obtainexternal data based upon one or more of the data packets received from awearable device 118 at a given time. The ARDS identifies one or morecharacteristics of a user's current total environment that the providingof an adaptive audio environment desirably addresses. For example, acurrent total environment involving fright conditions may result in anARDS providing a soothing adaptive audio environment.

As further shown in FIG. 2, the ARDS data may be provided by one or moremachine learning processes 218 to a sounds engine 108 configured toexecute one or more computer instructions facilitating sound adaptingprocesses 220. Based on ARDS data and information stored in the soundsdatabase 116, the sound adapting processes 220 may be configured toidentify one or more audible sounds that the wearable device 118 canoutput at a given time and/or for a given duration to provide a thendesired adaptive audio environment. Such audible sounds may be obtainedfrom the sounds database 116, from an external data source, or otherwiseand may include one or more sound forms including but not limited to,human voices, music, words, audible tracts, sirens, pitches, tones, orotherwise (collectively, “sounds”). Based on the ARDS data, the one ormore sound adapting processes 220 may be configured to identify thoseone or more sounds that match a current total environment and/or may beused to adjust a user's current total environment to a new desiredfuture total environment, for example, destressing a stressful situationby playing soothing sounds, versus increasing a user's adrenaline inanticipation of a sporting endeavor by providing upbeat sounds.

More specifically and for at least one embodiment, the sounds engine 108may include one or more sound adapting processes 220 configured toperform one or more operations including, but not limited to:identifying sounds available in the sounds database 116; identifyingsounds available from a remote database, such as one provided on one ormore remote storage devices 134; linking, organizing, and/or otherwiseprocessing sounds into one or more playlists, compilations or othercollections of sounds; parsing or otherwise associating sounds orportions thereof with various characteristics including, but not limitedto, tempo, speed, genre, artist, type, beats-per-minute, bit rate,sample rate, encoding format, number of channels available, storedvolume, duration, file size, profile, ownership status, composer, year,album, and the like; and otherwise identifying and associatingcharacteristics of sounds, or identifiable portions thereof, with one ormore ARDS data sets. For example, an ARDS data set indicative of aflight situation arising from a speeding ambulance, may result in thesounds adapting processes 220 identifying one or more siren sounds to beprovided from a given user's perspective, such as in-front, to the sideof or behind the user, with varying perspectives resulting in varyingsounds being identified to be output by the wearable device 118 at agiven time.

For at least one embodiment, the sound adapting processes 220 may beconfigured to generate audio output data 224 for providing to a wearabledevice 118 based upon one or more user preferences, such as thoseprovided by the user preferences database 114. User preferences mayindicate how, when, where, at what volume, and otherwise various soundsare to be generated. For example, sound adapting processes 220 associatewith a user that is tone deaf at particular frequencies may result inaudio output data 224 being generated that is different than a user thatis not so impaired. Likewise, user preferences for certain sounds mayvary by then arising environment. For example, a user may prefer “rock”songs over “country” songs when preparing for a sporting match.

For at least one embodiment, the sound adapting processes 220 may beconfigured to generate audio output data 224 for providing to a wearabledevice 118 based upon one or more contextual environments. For example,upon determining that a user is reading a particular novel, the soundsprovided by the wearable device 118 may change as the user progressesthrough the novel and/or in anticipation of such progression. Forexample, a first paragraph may invoke a calming sound being provided,while a next paragraph may invoke a triumphant or excited sound beingprovided. The determination of which sounds to presently and, in thefuture, provide, as specified in a given ARDS, may be based on the datapackets received from the wearable device and external data indicativeof the content of the novel at any desired level of granularity, such ason a chapter, page, paragraph, sentence or otherwise. In effect, one ormore sound adapting processes 220 for the various embodiments of thepresent disclosure may be configured to provide a moving sound scoresimilar to moving sound scores provided in association with televisionprogramming, movies, and otherwise.

The sound adapting processes 220 may be configured to output audiooutput data 224 from the hub 102 to one or more wearable device outputprocesses 226. Wearable device output processes 226 execute computerinstructions which facilitate the outputting of one or more soundsidentified in the audio output data 224 to a user and/or others by useof one or more audio devices, such as ear buds, headphones, speakers orthe like. The audio output data 224 may include an identification of asound to be provided to a user and, for at least one embodiment, theidentified sound itself. When only an identification is provided in theaudio output data 224, the sound may be provided by the wearable device118 using a sounds database (not shown) internal to the device and/orusing an external sounds database accessible by such wearable device118, such as a streaming sound source provided by a server 136.

The wearable device output processes 226 may further include computerinstructions for configuring the wearable device 118 to receive and/orsense user responses to the identified sounds provided in the audiooutput data 224. For example, user reactions to a sound such as their“like” or “dislike” thereof may be received and/or sensed. Such sensingmay occur using a physio sensor monitoring a user's brainwaves. Suchreactions may be provided as “feedback data” from the wearable device118 to the hub 102 and used by one or more of the hub processes 216,such as the machine learning processes 218 and sound adapting processes218, to refine future selections of audio output data 224.

For at least one embodiment, wearable device output processes 226 mayinclude computer instructions facilitating user control of providedadaptive audio environments based upon one or more user indicators inthe feedback data. Such user indicators may be detected by use of one ormore sensors, such as a sound sensor 122 detecting a user's spokecommands, e.g., “stop”, “rewind”, “skip”, “louder”, “softer”, or amotion sensor 127 detecting a user's change of orientation or otherwisein response to a given sound. Such data may be used to refine the hubprocesses 216.

For at least one embodiment, one or more of the machine learningprocesses 218 and/or sound adapting processes 220 may be configured toengage in machine learning, which herein is defined as the applicationof artificial intelligence to learn and improve the processes based onexperience and without being explicitly programmed. Machine learning mayinclude use of supervised machine learning algorithms, unsupervisedmachine learning algorithms, semi-supervised machine learningalgorithms, reinforcement machine learning algorithms and otherwise. Themachine learning processes 218 and/or sound adapting processes 220 maybe trained to use initial “default data”, such as one or more generictotal environment profiles and sounds corresponding therewith, to buildmathematical models of subsequent total environment profiles and soundscorresponding therewith. Such default data may be provided by the hub102 or external sources, such as server 136 or remote storage devices134. Machine learning processes may be used to personalize a hub andwearable device and the adaptive audio environments provided thereby fora given user and in view of one or more characteristics of a current ordesired future total environment.

In FIG. 3, one embodiment is shown of a process for providing anadaptive audio environment. In Operation 300, the process may begin withinitializing a wearable device and a hub. Initialization may involveloading default data into one or more of the user preferences databaseand/or sounds database, determining sensors available and configuringsuch one or more sensors for use with the given wearable device.

In Operation 302, the process may include determining whether userspecific data is available and to be utilized. This determining mayoccur on one or more of the wearable device 118 and the hub 102. Userspecific data may include user preferences, sounds, deviceconfigurations, and other information. It is to be appreciated thataudio output devices, sensors, and other components available to a usermay vary with time, location, activity or otherwise. Thus, Operation 302should be considered as including those determinations needed tocharacterize components (audio, sensors, data or otherwise) available toa user, by which and with which a system may provide an adaptive audioenvironment in view of a current or future total environment. Such userdata and/or other data may be available, for example, from a previouslypopulated user preferences database 114 and/or a previously populatedsounds database 116. The user data may include one or more tags,identifiers, metadata, user restrictions, use permissions, or otherinformation that identifies how and when such user data may be utilized.User data may also be available in the form of external data.

In Operation 304, the process may include obtaining the user data, inwhole or in part, for use by the wearable device 118 and/or the hub 102to generate audio output data 224 which provides an adaptive audioenvironment in view of a current or future total environment. While notexpressly shown in FIG. 3, it is to be appreciated that the obtaining ofuser data may be an iterative process, and such process(es) may be basedon a current total environment and/or a desired future total environmentinto which an adaptive audio environment is to be provided.

In Operation 306, the process may include obtaining sensor data from afirst sensor, such as an image sensor 120. Any of the available sensorsmay be selected as the first sensor. For at least one embodiment, datafrom a first sensor may be prioritized and used to initializecharacterize the current total environment, with data from other sensorsbeing used to further characterize the current total environment. Forother embodiments, sensor data may be weighted equally or as otherwisedesired.

In Operation 308, the process may include processing the first sensordata. Such processing may be accomplished using one or more computerinstructions provided by the sensor itself and/or one or more computerinstructions provided by one or more wearable device process inputprocesses 202.

In Operation 310, the process may include determining whether additionalsensor data is available and to be processed. If such additional sensordata is available and is to be processed, per Operation 312, the processmay include processing such additional sensor data. Such processing mayoccur using computer instructions provided by the sensor and/or one ormore wearable device input processes 202. It is to be appreciated thatone or more of Operations 308-310-312 may occur serially, in parallel,or otherwise. That is, for at least one embodiment a wearable device 118may be configured to simultaneously process data from multiple sensor atsubstantially the same time, wherein substantially as used in thiscontext refers to processing occurring without human perceptibledifferences in when such processing occurs.

In Operation 314, the process may include one or more data packetizationprocesses 214. As discussed above, such processes process one or moresensor data sets (as provided per Operations 308-310-312) into one ormore output data packets for communication to the hub 102. It is to beappreciated, that Operation 314 may arise repeatedly, iteratively, atany given time, or otherwise. For example, when data for a given sensoris ready for packetization and communication by a wearable device 118 toa hub 102, Operation 314 may occur with respect to only that availabledata, other available data, or otherwise.

In Operation 316, the process may include outputting one or more sets ofdata packets, by the wearable device 118, to the hub 102. The datapackets may be output in any desired form and for communication over adesired communications system. The data packets may be output in clearor encrypted form, compressed, uncompressed, error redundant, andotherwise. The form of a data packet may vary based on the type of databeing communicated, it being appreciated, for example, that image datamay require a larger packet size than audio data. The wearable device118 may be configured to schedule, prioritize, organize or otherwisecontrol the sequencing of data packets outputting and size and types ofcontent contained therein based upon then available sensor data and/orother variables. For example, the wearable device 118 may be configuredto prioritize the sending of certain audible data, such as sirens, overimage data.

In Operation 318, the process may include the hub 102 applying a firstof one or more machine learning processes. The machine learning processapplied may vary based on the size and type (e.g., image versus sound)of data provided in a given data packet. The one or more first machinelearning processes applied may also vary and/or be independent ordependent on a previously applied machine learning process. For example,analysis of image motion data in a then received data packet may involveuse of results and/or sub-processes applied for previously received datapackets that include image motion data.

In Operation 320, the process may include determining whether one ormore additional machine learning processes are to be applied to a datapacket. If so, per Operation 322, the process may include applying theone or more additional machine learning processes to one or more datapackets. It is to be appreciated that multiple machine learningprocesses may be applied individually, collectively, in aggregate orotherwise to a single or multiple data packets. Likewise, theapplication of a first machine learning process may result in adetermination to apply a second or more machine learning processes. Assuch, the various embodiments are not intended to be limited todetermining and/or applying any number of fixed or predetermined machinelearning processes. Further, the determining of whether to apply anadditional machine learning process may be optional where a given datapacket has been previously subjected to a machine learning process, inwhich instance, Operations 318-322 may be bypassed. Further and for atleast one embodiment, the determining of a machine learning process toapply may arise in view of external data, for example, weather dataindicative of impending severe weather may result in the processing of adata packet utilizing additional and/or other machine learning processesthan may occur when non-inclement weather is expected in a user'scurrent total environment or future total environment.

In Operation 324, the process may include generating an ARDS. Asdiscussed above, an ARDS identifies an action and one or more desirereactions thereto, where such reactions are expressed in terms of one ormore sound characteristics. The ARDS may be generated after applicationof one or more machine learning processes to one or more data packetsand/or external data. Multiple ARDS may be generated when dynamicsituations exist or, even when static conditions exist, dynamicconditions seek to be audible emulated.

In Operation 326, the process may include applying a first soundadapting process. The sound adapting process may be applied based on thegenerated ARDS data, based on external data, or based on other data. InOperations 328 and 330, the process may include applying one or moresecond sound adapting processes. It is to be appreciated that one ormore sound adapting processes may be utilized to generate audio outputdata.

In Operation 332, the process may include generating audio output data.The generated audio output data is provided by the hub 102 to thewearable device 118. For at least one embodiment, the generation of aaudio output data in response to sensor data happens substantiallyreal-time, as defined herein to mean without incurring humanlyperceptible delays and/or non-consequential humanly perceptible delays,where non-consequential humanly perceptible delays are delays that donot commonly result in an adverse condition or status arising withrespect to a given user of the wearable device 118.

In Operation 334, the process may include applying one or more outputprocesses to the audio output data. As used herein, output processescommonly result in the generation of one more sounds. For at least oneembodiment, such sounds are perceptible by the user of the wearabledevice 118. For at least one embodiment, such sounds are perceptible bya person other than and/or in addition to the user of the wearabledevice. For at least one embodiment, such sounds are perceptible bynon-human entities, such as animals, automated processes, artificialbeings, or otherwise.

In Operation 336, the process may include receiving user feedback inresponse to the application of the one or more output processes to theaudio output data. Such user feedback may arise based on physicalreactions, such as a verbal or physical indication that user“likes”/“dislikes” the sounds produced, cognitive reactions, such as theuser thinking positively or negatively about the sounds produced,non-cognitive reactions, such as the user reacting instinctively to thesounds produced, or otherwise. It is to be appreciated that a user'sreactions, or lack thereof, to sounds produced may be identified and/orassociated with the user's then current total environment, or acharacteristic thereof. Thus, it is to be appreciated that user feedbackto any given sound may vary over time and environment.

In Operation 338, the process may include updating user data based onthe feedback, or lack thereof, received per Operation 336. Updated userdata may be used for subsequent sensor data processing and may be usedto refine the providing of adaptive audio environments. Such updateddata may be used to refine, tune, modify, update or otherwise configureone or more machine learning processes and/or one or more sound adaptingprocesses. Accordingly, it is to be appreciated that at least oneembodiment, provides an closed loop system by which the providing ofadaptive audio environments “learns” and improves with time, events,total environments, and otherwise.

In Operations 340 and 342, the process may include determining whetherto cease providing an adaptive audio environment, and if so, ending suchproviding. The ending of such providing may arise based upon anyvariable, such as wearable device 118 or hub 102 power status, elapsedtime, user input, external data input, or otherwise.

The various operations shown in FIG. 3 are described herein with respectto at least one embodiment of the present disclosure. The describedoperations may arise in the sequence described, or otherwise and thevarious embodiments of the present disclosure are not intended to belimited to any given set or sequence of operations. Variations in theoperations used and sequencing thereof may arise and are intended to bewithin the scope of the present disclosure.

Although various embodiments of the claimed invention have beendescribed above with a certain degree of particularity, or withreference to one or more individual embodiments, those skilled in theart could make numerous alterations to the disclosed embodiments withoutdeparting from the spirit or scope of the claimed invention. The use ofthe terms “approximately” or “substantially” means that a value of anelement has a parameter that is expected to be close to a stated valueor position. However, as is well known in the art, there may be minorvariations that prevent the values from being exactly as stated.Accordingly, anticipated variances, such as 10% differences, arereasonable variances that a person having ordinary skill in the artwould expect and know are acceptable relative to a stated or ideal goalfor one or more embodiments of the present disclosure. It is also to beappreciated that the terms “top” and “bottom”, “left” and “right”, “up”or “down”, “first”, “second”, “next”, “last”, “before”, “after”, andother similar terms are used for description and ease of referencepurposes only and are not intended to be limiting to any orientation orconfiguration of any elements or sequences of operations for the variousembodiments of the present disclosure. Further, the terms “coupled”,“connected” or otherwise are not intended to limit such interactions andcommunication of signals between two or more devices, systems,components or otherwise to direct interactions; indirect couplings andconnections may also occur. Further, the terms “and” and “or” are notintended to be used in a limiting or expansive nature and cover anypossible range of combinations of elements and operations of anembodiment of the present disclosure. Other embodiments are thereforecontemplated. It is intended that all matter contained in the abovedescription and shown in the accompanying drawings shall be interpretedas illustrative only of embodiments and not limiting. Changes in detailor structure may be made without departing from the basic elements ofthe invention as defined in the following claims.

Further, a reference to a computer executable instruction includes theuse of computer executable instructions that are configured to perform apredefined set of basic operations in response to receiving acorresponding basic instruction selected from a predefined nativeinstruction set of codes. It is to be appreciated that such basicoperations and basic instructions may be stored in a data storage devicepermanently and/or may be updateable, but, are non-transient as of agiven time of use thereof. The storage device may be any deviceconfigured to store the instructions and is communicatively coupled to aprocessor configured to execute such instructions. The storage deviceand/or processors utilized operate independently, dependently, in anon-distributed or distributed processing manner, in serial, parallel orotherwise and may be located remotely or locally with respect to a givendevice or collection of devices configured to use such instructions toperform one or more operations.

What is claimed is:
 1. A system for providing an adaptive audioenvironment, comprising: a wearable device; and a hub, comprising: aninterface module configured to communicatively couple the wearabledevice and the hub; and a processor, configured to execute non-transientcomputer executable instructions for: a machine learning engineconfigured to: apply a first machine learning process to at least onedata packet received from the wearable device;  wherein, based on the atleast one data packet, the machine learning process is operable toidentify a current environment and a future arising environment; wherein the future arising environment is an environment not presentlypresented to a user of the wearable device; and output anaction-reaction data set; wherein the action-reaction data set is basedon the future arising environment; wherein the action-reaction data setis further based on a probability of each of a non-presently occurringphysio data, position data and motion data for the user exceeding aprobability threshold of occurring in the future arising environment;and a sounds engine configured to apply a sound adapting process to theaction-reaction data set and provide audio output data to the user ofthe wearable device via the interface module; and wherein the audiooutput data is utilized by the wearable device to provide the user withan adaptive audio environment based upon the future arising environment.2. The system of claim 1, wherein the wearable device is configured toexecute non-transient computer instructions for a wearable device inputprocess configured to process first sensor data received from a firstsensor.
 3. The system of claim 2, wherein the first sensor data includesimage data.
 4. The system of claim 2, wherein the wearable device inputprocess is further configured to process at least one second sensor datareceived from at least one second sensor.
 5. The system of claim 4,comprising: wherein each of the first sensor data and the at least onesecond sensor data is provided by at least one of an image sensor, asound sensor, a physio sensor, a location sensor, and a motion sensor.6. The system of claim 5, wherein the wearable device input processincludes applying a data packetization process to each of the firstsensor data and the at least one second sensor data; and wherein aresult of the data packetization process is output by the wearabledevice to the hub as the at least one data packet.
 7. The system ofclaim 1, the wearable device comprising: at least one sensor; and atleast one audio output device.
 8. The system of claim 7, wherein themachine learning engine is further configured to use feedback data whenapplying a second machine learning process to at least one second datapacket received from the wearable device and output a secondaction-reaction data set; wherein the sounds engine is furtherconfigured to apply a second sound adapting process to the secondaction-reaction data set and provide second audio output data to thewearable device via the interface module; and wherein the second audiooutput data is further utilized by the wearable device and the at leastone audio output device to provide the adaptive audio environment. 9.The system of claim 7, wherein the wearable device is configured toexecute non-transient computer instructions for a wearable device outputprocess; wherein the wearable device output process processes the audiooutput data for presentation to a user by the at least one audio outputdevice.
 10. The system of claim 1, wherein the interface module isfurther configured to communicatively couple the hub with at least oneserver.
 11. The system of claim 9, wherein a server communicatesexternal data to the hub; and wherein, based on the external data, themachine learning engine is further configured to apply a second machinelearning process to the at least one data packet received from thewearable device and output a second action-reaction data set.
 12. Asoftware architecture, encoded on at least one non-transitory computerreadable medium for providing an adaptive audio environment, comprising:a wearable device input process comprising non-transient computerinstructions, executable by a wearable device, for: processing firstsensor data received from a first sensor and outputting at least onedata packet; wherein the processing of the first sensor data comprisesidentifying a future arising environment; wherein the future arisingenvironment is an environment not presently presented to a user of thewearable device; and wherein the at least one data packet is outputbased on a probability of each of a non-presently occurring physio data,position data and motion data for the user exceeding a probabilitythreshold of occurring in the future arising environment; a wearabledevice output process comprising non-transient computer instructions,executable by the wearable device, for receiving and processing audiooutput data into at least one sound for use in providing a futurearising adaptive audio environment to the user of the wearable device;wherein the audio output data is received from and results from aprocessing of the at least one data packet by the hub.
 13. The softwarearchitecture of claim 12, wherein the first sensor data is received fromat least one of an image sensor, a sound sensor, a physio sensor, alocation sensor, and a motion sensor.
 14. The software architecture ofclaim 12 comprising: a machine learning process comprising non-transientcomputer instructions, executable by the hub, for processing the atleast one data packet into an action-reaction data set; a sound adaptingprocess comprising non-transient computer instructions, executable bythe hub, for processing the action-reaction data set into audio outputdata.
 15. The software architecture of claim 14, wherein the machinelearning processes utilizes user preference data stored in a userpreference database to process the at least one data packet in anaction-reaction data set; and wherein the sound adapting processutilizes sound data stored in a sounds database to process theaction-reaction data set into the audio output data.
 16. The softwarearchitecture of claim 15, wherein at least one of the machine learningprocess and the sound adapting process comprise at least one supervisedmachine learning algorithm trained using an initial default data setcorresponding to a generic total environment.
 17. A method for providingan adaptive audio environment, comprising: in a wearable device,receiving sensor data; processing the sensor data using at least onewearable device input process; wherein the processing of the sensor datacomprises identifying a future arising environment; wherein the futurearising environment is an environment not presently presented to a userof the wearable device; generating, based on the processing, a datapacket; wherein the data packet is generated based on a probability ofeach of non-presently occurring physio data, position data and motiondata for the user exceeding a probability threshold of occurring in thefuture arising environment; outputting the data packet to a hub device;wherein the data packet identifies the future arising environment;receiving audio output data from the hub device; and applying at leastone wearable device output process to the received audio output data toproduce at least sound providing an adaptive audio environment for theuser; and in a hub device, receiving the data packet from the wearabledevice; applying in view of the identified future arising environment,at least one hub process to the data packet; and outputting the audiooutput data, wherein the audio output data results from the applying ofthe at least one hub process to the data packet; wherein the applying ofthe at least one hub process occurs based at least upon the identifiedfuture arising environment.
 18. The method of claim 17, wherein thesensor data includes first sensor data received from an image sensor andsecond sensor data received from at least one of a sound sensor, aphysio sensor, a location sensor and a motion sensor.
 19. The method ofclaim 18, wherein the at least one sound is responsive to an imageprovided in the first sensor data.
 20. The method of claim 19, whereinthe second sensor data includes physio data for the user; and whereinthe at least one sound is responsive to the physio data.